def AlignImagesWithFiducials(img1,img2,xf1,yf1,xf2,yf2):
"""
Aligns img2 to img1 based on an array listing the x,y coordinates of common fiducials.
Arguments:
img1 - the reference image to be aligned to.
img2 - the image to be aligned to the reference image.
xf1 - an array containing the x coordinates of the fiducials in img1
yf1 - an array containing the y coordinates of the fiducials in img1.
xf2 - an array containing the x coordinates of the fiducials in img2.
yf2 - an array containing the y coordinates of the fiducials in img2.
Returns:
newimg - img2 as aligned and interpolated to the coordinates of img1.
"""
#Match them
tx,ty,theta,mag = matchFiducials_wMag(yf1,xf1,yf2,xf2)
x2_wNaNs,y2_wNaNs,z2_wNaNs = man.unpackimage(img2,remove = False,xlim=[0,np.shape(img2)[1]],\
ylim=[0,np.shape(img2)[0]])
#Apply transformations to x,y coords
x2_wNaNs,y2_wNaNs = transformCoords_wMag(x2_wNaNs,y2_wNaNs,ty,tx,theta,mag)
#Get x,y,z points from reference image
x1,y1,z1 = man.unpackimage(img1,remove=False,xlim=[0,np.shape(img1)[1]],\
ylim=[0,np.shape(img1)[0]])
#Interpolate stitched image onto expanded image grid
newimg = griddata((x2_wNaNs,y2_wNaNs),z2_wNaNs,(x1,y1),method='linear')
print 'Interpolation ok'
newimg = newimg.reshape(np.shape(img1))
#Images should now be in the same reference frame
#Time to apply tip/tilt/piston to minimize RMS
newimg = matchPistonTipTilt(img1,newimg)
return newimg
def stitchImages(img1,img2):
"""Allows user to pick fiducials for both images.
Function then computes the transform to move img2
to img1 reference frame.
Updated
"""
#Get both fiducials
xf1,yf1 = getPoints(img1)
xf2,yf2 = getPoints(img2)
#Match them
tx,ty,theta = matchFiducials(xf1,yf1,xf2,yf2)
#Pad img1 based on translations
img1 = man.padNaN(img1,n=round(tx),axis=1)
img1 = man.padNaN(img1,n=round(ty),axis=0)
#Shift img1 fiducial locations
if tx<0:
xf1 = xf1 - tx
if ty<0:
yf1 = yf1 - ty
#Get x,y,z points from stitched image
x2,y2,z2 = man.unpackimage(img2,xlim=[0,shape(img2)[1]],\
ylim=[0,shape(img2)[0]])
#Apply transformations to x,y coords
x2,y2 = transformCoords(x2,y2,ty,tx,theta)
#Get x,y,z points from reference image
x1,y1,z1 = man.unpackimage(img1,remove=False,xlim=[0,shape(img1)[1]],\
ylim=[0,shape(img1)[0]])
#Interpolate stitched image onto expanded image grid
newimg = griddata((x2,y2),z2,(x1,y1),method='linear')
print 'Interpolation ok'
newimg = newimg.reshape(shape(img1))
#Images should now be in the same reference frame
#Time to apply tip/tilt/piston to minimize RMS
newimg = matchPistonTipTilt(img1,newimg)
#Would like list of enlarge image showing all valid data, this is final step
#Avoid overwritting fiducials
#Save indices of NaNs near fiducials
find = logical_and(sqrt((y1-xf1[0])**2+(x1-yf1[0])**2) < 15.,\
isnan(img1).flatten())
for i in range(1,size(xf1)):
find = logical_or(find,\
logical_and(sqrt((y1-xf1[i])**2+(x1-yf1[i])**2) < 15.,\
isnan(img1).flatten()))
#Include newimg data
img1[isnan(img1)] = newimg[isnan(img1)]
#Reset fiducials to NaNs
img1[find.reshape(shape(img1))] = NaN
#Return translations to automatically pad next image
return img1,tx,ty
